Publications by Bob Carpenter
R and OOP anti-patterns
Thomas Lumley just dropped a blog post, Blank cheque inheritance and statistical objects, which begins as follows. One of the problems with object-oriented programming for statistical methods is that inheritance is backwards. Everything is fine for data structures, and Bioconductor has many examples of broad (often abstract) base classes for biolo...
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ChatGPT can write talks about brms and run a D&D game
A year ago, at the tail end of non-AI-enabled humanity, Andrew asked what’s going on with the reports of chatbot potential for large language models like Google’s LaMDA and OpenAI’s GPT-3. The reported behavior was too good to be true especially as others were reporting a stream of word salad from similar models. So Andrew put out the above-...
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BridgeStan: Stan model log densities, gradients, Hessians, and transforms in R, Python, and Julia
We’re happy to announce the official 1.0.0 release of BridgeStan. What is BridgeStan? From the documentation: BridgeStan provides efficient in-memory access through Python, Julia, and R to the methods of a Stan model, including log densities, gradients, Hessians, and constraining and unconstraining transforms. BridgeStan should be useful fo...
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Just show me the data, baseball edition
Andrew’s always enjoining people to include their raw data. Jim Albert, of course, does it right. Here’s a recent post from his always fascinating baseball blog, Exploring Baseball Data with R, 49 seasons of the designated hitter. The post “just” plots the raw data and does a bit of exploratory data analysis, concluding that the appar...
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Super Sam Fuld Needs Your Help (with Foul Ball stats)
I was pleasantly surprised to have my recreational reading about baseball in the New Yorker interrupted by a digression on statistics. Sam Fuld of the Tampa Bay Rays, was the subjet of a Ben McGrath profile in the 4 July 2011 issue of the New Yorker, in an article titled Super Sam. After quoting a minor-league trainer who described Fuld as “a ...
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Martyn Plummer’s Secret JAGS Blog
Martyn Plummer, the creator of the open-source, C++, graphical-model compiler JAGS (aka “Just Another Gibbs Sampler”), runs a forum on the JAGS site that has a very similar feel to the mail-bag posts on this blog. Martyn answers general statistical computing questions (e.g., why slice sampling rather than Metropolis-Hastings?) and general mo...
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A Stan is Born
Stan 1.0.0 and RStan 1.0.0 It’s official. The Stan Development Team is happy to announce the first stable versions of Stan and RStan. What is (R)Stan? Stan is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo. It’s sort of like BUGS, but with a different language for ...
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Stan and RStan 1.1.0
We’re happy to announce the availability of Stan and RStan versions 1.1.0, which are general tools for performing model-based Bayesian inference using the no-U-turn sampler, an adaptive form of Hamiltonian Monte Carlo. Information on downloading and installing and using them is available as always from Stan Home Page: http://mc-stan.org/ Let...
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Stan 1.2.0 and RStan 1.2.0
Stan 1.2.0 and RStan 1.2.0 are now available for download. See: http://mc-stan.org/ Here are the highlights. Full Mass Matrix Estimation during Warmup Yuanjun Gao, a first-year grad student here at Columbia (!), built a regularized mass-matrix estimator. This helps for posteriors with high correlation among parameters and varying scales. We�...
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Stan 1.3.0 and RStan 1.3.0 Ready for Action
The Stan Development Team is happy to announce that Stan 1.3.0 and RStan 1.3.0 are available for download. Follow the links on: Stan home page: http://mc-stan.org/ Please let us know if you have problems updating. Here’s the full set of release notes. v1.3.0 (12 April 2013) ====================================================================...
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